Healthcare

IEEE Transactions on Affective Computing Journal (Q1 - IF 9.6) Special Issue on Affective Impact of Next-Generation Intelligent Health Systems

Background: Interdepartmental consultations are essential for managing complex inpatient care but are often inefficient. Hospital-wide, data-driven analyses are needed to guide process improvements; yet, most existing studies have focused on single departments or specific diseases, leaving a gap in understanding hospital-level collaboration networks. Understanding these patterns is crucial for optimizing clinical workflows, reducing delays, and improving patient outcomes in large tertiary hospit

Background: Large language models (LLMs) show growing potential for decision support. However, integrating domain-specific medical knowledge while maintaining accuracy, safety, and interpretability remains challenging for postoperative discharge instructions and patient education. Fine-tuning, retrieval-augmented generation (RAG), and hybrid fine-tuning+RAG approaches are prominent strategies for knowledge integration, but their comparative performance in postoperative care has not been systemat

Background: As artificial intelligence (AI) chatbots become an increasingly common source of quick medical guidance, it is important to understand whether their responses meet users’ needs and support well-informed health decisions. Yet, existing evaluation frameworks rely primarily on expert-defined evaluation dimensions that have not been empirically validated with end users. It remains unclear whether these frameworks capture the criteria people actually use when judging a response to be usef

Clinical notes contain many of the signs and symptoms that bring patients to care, yet this information rarely reaches structured fields. Existing extraction approaches either rely on context-insensitive rules that generate false positives or on supervised models that require substantial fine-tuning. We present Pythia, a multi-agent system that autonomously writes and optimizes extraction prompts for clinical concepts without manual prompt engineering or fine-tuning. Running on a locally hosted

McConnell's hospitalization, Trump's health opacity, and now a senator's sudden death are forcing a GOP reckoning.

The fund-raising values the company at $3.8 billion and underscores investor interest in using artificial intelligence to tackle problems like drug discovery.

The pitch for medical AI is that it frees clinicians to care for patients. Two stories this week suggest the reality can run the other way. In New York, nurses say software replaced them. In Minnesota, a former Mayo Clinic leader says the software was not safe to trust. Marilyn Shuler spent 39 years reading […] This story continues at The Next Web

InterSystems, a creative data technology provider powering more than one billion health records globally, today announced it has been recognized as a Leader in the 2026 Gartner Magic Quadrant for Enterprise Electronic Health Records (EHR).

Generative AI’s overemphasis on Western moral concerns could reinforce global disparities in sensitive applications such as public health messaging and global communication.

Settlement will drive down patients’ out-of-pocket costs, increase transparency and ensure community pharmacies are treated fairly The Federal Trade Commission secured a settlement agreement with one of the nation’s largest pharmacy benefit managers (PBMs) and its affiliated entities, marking yet another important victory in the Commission’s fight to lower healthcare costs for Americans. View Press Release

The modern soybean is one of the world’s miracle technologies, capable of cheaply, efficiently, and healthfully supplying protein and other nutrients to billions of people. But, unfortunately, we’ve mostly chosen to squander it on the most destructive purposes possible. Soy is America’s second most widely cultivated crop, occupying a land area equivalent to more than […]

This past year, Zhihao Lei has signed his name to at least 35 letters to the editor of medical journals, weighing in on topics from ICU care to breast cancer. In eight of them, he identified himself as a Ph.D. at Cornell University, although he held only a bachelor’s degree at the time. Two of … Continue reading A student claimed to have a Ph.D. in at least eight letters to journals. Two have been retracted.

Deepfakes undermine the credibility that makes digital care — from telehealth visits to patient portals — possible.

The results of a recent AI competition show that better data trumps bigger models when it comes to predicting properties of drug candidates.

When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while overlooking a more fundamental question: how should sample usefulness for classification be measured and optimized? We address this with Class-Contrastive Influence (C2I), a criterion

As Kaiser Permanente CEO Greg Adams touted the success of his organization’s value-based care models at the AHA Leadership Summit in Denver, Kaiser nurses outside protested the health system’s AI strategy. They claim they’re being excluded from decisions about the technology. The post Kaiser Nurses Protest CEO’s AHA Appearance Over AI Concerns appeared first on MedCity News .

Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation. A prevalent challenge in mitigating this issue arises in non-English contexts and low socioeconomic classes, where limited data hinders the training of AI models for effective detection. Consequently, culturally and linguistically diverse (CALD) commun

Information sharing between Home Office and NHS England has led to leaks of sensitive data

arXiv:2607.10163v1 Announce Type: cross Abstract: We study recovery of a low-rank tensor $\mathcal{T}$ and a low-rank matrix $M$ from sparse, noisy observations. $\mathcal{T}$ and $M$ share one mode. We relax tensor rank using the nuclear norm of the mode-1 unfolding. This unfolding carries the coupling. It also has an exact proximal operator. We couple $\mathcal{T}$ and $M$ through a learned linear operator $G$. We prove a minimizer exists for the ridge-stabilized penalized objective. We prove

Facing anti-corruption policies and profit squeezing at home, Chinese innovative medical device companies are accelerating their push into the European market despite mounting cross-border trade protectionism. Government-backed hospital purchases of medical devices in China fell about 12 per cent year on year in the first five months of 2026, partly weighed down by “a new anti-corruption probe into hospitals”, dragging on the revenue of major Chinese medical device makers in the first half, said

See how sales teams can use ChatGPT Work to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.

On April 18, 2026, President Trump issued Executive Order (E.O.) 14401, "Accelerating Medical Treatments for Serious Mental Illness", acknowledging that individuals suffering from serious mental illness may not always respond to existing therapies. This request for information (RFI) solicits stakeholder feedback on training and care delivery models that could be used to ensure safe and effective delivery of potential future Food and Drug Administration (FDA)- approved psychedelic drugs, includin

As a pivotal technique in smart healthcare, medical image fusion integrates complementary functional and structural information to facilitate accurate diagnosis and enhance clinical decision-making reliability. However, existing centralized methods typically raise serious data privacy concerns, while standard distributed approaches often fail to balance global generalization with local node personalization due to data heterogeneity. To address this, we propose FLMMIF, a privacy-preserving framew

July 14 (Reuters) - Twenty-six employees of Meta ‌Platforms have filed a novel lawsuit accusing the tech giant of using AI-powered software that disproportionately targeted people with disabilities or who took medical leave in selecting wor ... (https://incidentdatabase.ai/cite/1584#7510)

BY THE PRESIDENT OF THE UNITED STATES OF AMERICA A PROCLAMATION 1. The United States relies on a strong chemical manufacturing sector to support industries like energy, national defense, agriculture, and health care. These facilities produce essential inputs for critical infrastructure, advanced manufacturing, medical sterilization, semiconductors, and national defense systems. Maintaining a robust domestic chemical […] The post Regulatory Relief for Certain Stationary Sources to Promote America

Two recently published viewpoint articles in JMIR highlighted the two faces of medical informatics research. On the one hand, they spotlight the significant advances digital technologies bring to health management and delivery. Technological advances of the last decade have transformed healthcare worldwide. Consumers have access to digital health tools, gathering an unprecedented amount of data available for gaining personalized insights. Digital technologies support medical professionals in cli

Background: Patient-reported outcomes in digital health solutions can offer patients with type 1 diabetes an opportunity to voice their needs in outpatient care, enabling clinicians to tailor support. Evidence on long-term health impact and routine integration of such digital solutions outside controlled settings is limited. Objective: This study aimed to compare a flexible digital supplement to outpatient care for type 1 diabetes (DigiDiaS) with usual care over 1 year, with self-management as t

Background: Problem-solving is essential for the self-management of type 2 diabetes but remains challenging for underserved individuals. Although mobile health (mHealth) interventions can improve diabetes self-management, few focus on problem-solving. Objective: This study evaluates the efficacy of Mobile Diabetes Detective (MoDD), a fully automated web-based intervention with SMS text messaging that provides problem-solving support tailored to self-monitoring data, for improving glycemic contro

Between 2023 and 2026, frontier AI systems crossed documented human expert baselines on a growing set of bounded, well-specified, evaluable cognitive tasks, including graduate-level science questions, competition mathematics, software-engineering benchmarks, and structured diagnostic reasoning, while the length of tasks such systems can complete at 50% reliability doubled roughly every seven months. These crossings are rapid and broad, but the frontier is jagged: humans retain decisive advantage

Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations. We present ChartCynics, an agentic dual-path framework designed to unmask visual deception via a "skeptical" reasoning paradigm. Unlike holistic models, ChartCynics decouples perception from verification: a Diagnostic Vision Path captures structural anomalies (e.g., inverted axes) through strategic ROI cropping, while

Maine joined 15 states on Friday in suing the Trump administration to prevent millions of dollars in cuts to school-based mental health funding. The new lawsuit is part of an ongoing legal battle between Democratic-led states and the U.S. Department of Education over a mental health grant program that Congress established following the 2018 school […]

This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture combines parameter-efficient modality-specific adaptation with sequential fusion, enabling modalities to be integrated in stages without retraining previously learned components. Rather than assuming a fixed fusion structure, the framework first integrates more closely related modalities and then in

Welsh Affairs Committee: cross-border healthcare patients are “falling through the gaps” The Welsh Affairs Committee is concerned by the lack of urgency in addressing long-standing issues affecting ...

The Anomalous Health Incidents cross-functional team has been renamed as officials focus on "non-kinetic threats." The post Pentagon disburses Havana Syndrome compensation, rebrands team focused on ‘Directed Energy Bio-Effects’ appeared first on DefenseScoop .

State-sponsored attackers are targeting critical infrastructure networks in defense, communications, energy, finance, government and health care. The post Officials once again warn defenders that Russian hackers are targeting network devices appeared first on CyberScoop .

Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not designed for clinical simulation can reproduce these patterns remains untested. We investigated (1) whether lesions or controlled perturbations to a multimodal language model can reproduce different types of errors in picture naming, and (2) whether the framework can reproduce the complete error profile of individual persons with aphasia (PWAs). Using

The spread of hate speech (HS) across different social media platforms (SMPs) poses a major concern for online safety and ethical moderation. Automatic detection of HS remains a challenging task, especially in under-resourced languages like Bangla, due to cultural context, implicit expressions, and informal linguistic patterns. This study aimed to expose the crisis of Bangla HS detection systems by diagnosing how and why benchmark-trained models fail to identify implicit, context-dependent HS. S

Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings. We present DiffEEG, a 9.6M-parameter self-supervised foundation model that addresses both limitations through denoising diffusion pre-training and reinforcement learning (RL)-based fine-tuning. Pre-trained on 1.3M unlabeled segments from the Temple University Hospital Seizure Corpus (TUHSZ), DiffEEG lea